Francesca Margara

Francesca Margara
University of Oxford | OX · Department of Computer Science

Doctor of Philosophy

About

20
Publications
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Introduction

Publications

Publications (20)
Article
Full-text available
Hypertrophic cardiomyopathy (HCM), the most common inherited heart disease, is still orphan of a specific drug treatment. The erroneous consideration of HCM as a rare disease has hampered the design and conduct of large, randomized trials in the last 50 years, and most of the indications in the current guidelines are derived from small non-randomiz...
Preprint
Full-text available
Cardiomyopathies have unresolved genotype-phenotype relationships and lack disease-specific treatments. Here we identify genotype-specific pathomechanisms and therapeutic targets combining experimental hiPSC-CM modelling and human-based cardiac electromechanical in-silico modelling and simulation bridging from specific mutations to clinical biomark...
Article
Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): BHF Background Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) enable accessible human data-based cardiology studies. However, a caveat in hiPSC-CM-based studies is their immature electrophysiological and contractile phenotype. One of the mo...
Article
Hypertrophic cardiomyopathy (HCM) affects as many as ~1 in 500 individuals, and is often typified by hyperdynamic contraction and poor cellular relaxation. HCM can be caused by mutations in a variety of key contractile proteins of the sarcomere. A large proportion of these variants are found in MYBPC3, MYH7, TNNT2, and TNNI3. These genes encode pro...
Article
Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement 764738. British Heart Foundation Intermediate Basic Science Fellowship (FS/17/22/32644). Background The pathogenic TNNI3R21C/+ varia...
Article
Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NC3Rs Infrastructure for Impart Award (NC/P001076/1) Wellcome Trust Senior Research Fellowship in Basic Biomedical Sciences (214290/Z/18/Z) Background Human-based computer modelling and simulations have been widely used in cardiac elec...
Article
Full-text available
Rationale: Calcium transient analysis is central to understanding inherited and acquired cardiac physiology and disease. While the development of novel calcium reporters enables assays of CRISPR/Cas-9 genome edited pluripotent stem cell derived cardiomyocytes (iPSC-CMs) and primary adult cardiomyocytes, existing calcium-detection technologies are o...
Article
Full-text available
Develop, calibrate and evaluate with clinical data a human electromechanical modelling and simulation framework for multiscale, mechanistic investigations in healthy and post-myocardial infarction (MI) conditions, from ionic to clinical biomarkers. Human healthy and post-MI electromechanical simulations were conducted with a novel biventricular m...
Article
Full-text available
Human-based computational modelling and simulation are powerful tools to accelerate the mechanistic understanding of cardiac patho-physiology, and to develop and evaluate therapeutic interventions. The aim of this study is to calibrate and evaluate human ventricular electro-mechanical models for investigations on the effect of the electro-mechanica...
Article
Full-text available
The human heart beats as a result of multiscale nonlinear dynamics coupling subcellular to whole organ processes, achieving electrophysiologically-driven mechanical contraction. Computational cardiac modelling and simulation have achieved a great degree of maturity, both in terms of mathematical models of underlying biophysical processes and the de...
Article
Full-text available
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twi...